Limited Labeled Data

Limited labeled data poses a significant challenge across numerous machine learning applications, hindering the development of accurate and robust models. Current research focuses on leveraging unlabeled data through semi-supervised and self-supervised learning techniques, often employing Bayesian neural networks, graph neural networks, and various active learning strategies to maximize information gain from limited annotations. These methods aim to improve model performance and stability in scenarios with scarce labeled data, impacting fields ranging from energy management and personalized AI assistants to medical image analysis and hate speech detection. The ultimate goal is to develop reliable and efficient machine learning solutions that can operate effectively even with limited human-labeled training examples.